One or more Applications - virtual experiments that can be simulated: initial conditions, actual morphologies, electrical protocols, etc.

An Application can be compartmental or it can use a Geometry (see below).

Each Application has a mathematical representation that is automatically generated and can be viewed.

For each Application you can specify one or more

Simulations (time length, resolution, solvers to use, parameter overrides, etc.) that will run and produce Results. Results can be viewed in VCell or exported to a variety of formats.

MathModel is somewhat equivalent to the math description of a BioModel Application – it is a direct specification of the equations to be solved, using the VCell modeling language. It can be compartmental or it can use a spatial Geometry. One or more Simulations must be defined for each MathModel to run and produce Results.

The math from a BioModel Application is a valid MathModel, but MathModel can not be extended to a BioModels. MathModels are needed to override biology-driven limitations of BioModels.

Geometry is a representation of a spatial structure which a BioModel Application (or a MathModel) can use for spatially resolved simulation. It can be 1-D, 2-D, or 3-D, and either analytically defined or based on a digital image.

Capabilities

Non-Spatial Deterministic (ODE) Solvers

Forward Euler (First Order, Fixed Time Step)

Runge-Kutta (Second Order, Fixed Time Step)

Runge-Kutta (Fourth Order, Fixed Time Step)

Adams-Moulton (Fifth Order, Fixed Time Step)

Runge-Kutta-Fehlberg (Fifth Order, Variable Time Step)

IDA (Variable Order, Variable Time Step, ODE/DAE)

CVODE (Variable Order, Variable Time Step)

Combined stiff solver CVODE/IDA

Spatial Deterministic (PDE) Solvers

Semi-Implicit Finite Volume, Regular Grid (Fixed Time Step)

Semi-Implicit Finite Volume Compiled, Regular Grid (Fixed Time Step)

Fully-Implicit Finite Volume, Regular Grid (Fixed Time Step).

Non-Spatial Stochastic Solvers

Gibson (Next Reaction Stochastic Method)

Hybrid (Gibson + Euler-Matuyama Method)

Hybrid (Gibson + Milstein Method)

Hybrid (Adaptive Gibson + Milstein Method)

Spatial Stochastic Solvers

(Only available in VCell 5.1 Beta version or later)
The Virtual Cell incorporates the COPASI parameter estimation capabilites to optimize parameters in non-spatial deterministic models to best fit experimental data. The available optmization solvers are listed below:

Evolutionary Programming

Evolution Strategy (SRES)

Genetic Algorithm

Genetic Algorithm SR

Hooke and Jeeves

Levenberg - Marquardt

Nelder - Mead

Particle Swarm

Random Search

Simulated Annealing

Steepest Descent

Praxis

Truncated Newton

Model specification

Comaprtmental volumes and membrane sizes

Concentrations and/or molecule counts for species

3D Diffusion

2D Membrane Diffusion

Advection (Velocity)

Local and global parameters

Initial conditions can be specified in terms of other model parameters, reserved symbols and species.